Several compositional distributional semantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we investigate whether we can match the performance of full tensors with low-rank approximations that use a fraction of the original number of parameters. We investigate the effect of low-rank tensors on the transitive verb construction where the verb is a third-order tensor. The results show that, while the low-rank tensors require about two orders of magnitude fewer parameters per verb, they achieve performance comparable to, and occasionally surpassing, the unconstrained-rank tensors on sentence similarity and verb disambiguation tasks.
CITATION STYLE
Fried, D., Polajnar, T., & Clark, S. (2015). Low-rank tensors for verbs in compositional distributional semantics. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 731–736). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2120
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